Cvt W24 384 22k

🚀 Convolutional Vision Transformer (CvT)

A CvT - w24 model pre - trained on ImageNet - 22k and fine - tuned on ImageNet - 1k at 384x384 resolution, offering high - performance image classification.

🚀 Quick Start

The CvT - w24 model was introduced in the paper CvT: Introducing Convolutions to Vision Transformers by Wu et al. and first released in this repository. It's pre - trained on ImageNet - 22k and fine - tuned on ImageNet - 1k at a resolution of 384x384.

Disclaimer: The team releasing CvT did not write a model card for this model, so this model card has been written by the Hugging Face team.

💻 Usage Examples

Basic Usage

# Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
from transformers import AutoFeatureExtractor, CvtForImageClassification
from PIL import Image
import requests

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

feature_extractor = AutoFeatureExtractor.from_pretrained('microsoft/cvt-w24-384-22k')
model = CvtForImageClassification.from_pretrained('microsoft/cvt-w24-384-22k')

inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])

📄 License

This project is under the Apache - 2.0 license.